Noise Enhancement Research Articles

APPLICATION OF THE ALGORITHM OF PARAMETRIC AND NON-PARAMETRIC CONFIDENCE INTERVALS IN PRE-PROCESSING IMAGE DATA

Digital image processing and enhancement is one of the most important and frequently used issues in many fields of image processing. When handling images or sending them over a particular channel, they are subject to certain noise and require filtering methods. In this paper, the parametric confidence interval algorithm was compared to the nonparametric confidence interval algorithm for processing the noisy images. The results showed that a nonparametric confidence interval algorithm is better at defining the external parameters of an image in terms of noise elimination and enhancement landmarks.

Inductor-capacitor passive wireless sensors using nonlinear parity-time symmetric configurations

An inductor–capacitor passive wireless sensor is essential to physical, chemical, and biological sensing for scenarios where physical access is difficult. Exceptional points of parity-time symmetric inductor–capacitor systems featuring the linear loss and gain have been utilized for enhancing sensing. However, the exceptional point sensing scheme might bring about fundamental resolution limits and noise enhancement. Here we show, employing a nonlinear saturable gain, the responsivity has a cube-root singularity distinct from a square-root singularity of the linear exceptional point scheme. The saturable gain eliminates the imaginary part of the eigenfrequencies and significantly suppresses the noise. Through an example of inductor–capacitor wireless wearable temperature sensors, we demonstrate the high figure of merit for the nonlinear PT-symmetric configuration. Our results resolve a debate on the effectiveness of the exceptional point sensing scheme for inductor–capacitor sensors and provide a way of enhancing precision for these types of sensors.

Adversarial Spatiotemporal Contrastive Learning for Electrocardiogram Signals.

Extracting invariant representations in unlabeled electrocardiogram (ECG) signals is a challenge for deep neural networks (DNNs). Contrastive learning is a promising method for unsupervised learning. However, it should improve its robustness to noise and learn the spatiotemporal and semantic representations of categories, just like cardiologists. This article proposes a patient-level adversarial spatiotemporal contrastive learning (ASTCL) framework, which includes ECG augmentations, an adversarial module, and a spatiotemporal contrastive module. Based on the ECG noise attributes, two distinct but effective ECG augmentations, ECG noise enhancement, and ECG noise denoising, are introduced. These methods are beneficial for ASTCL to enhance the robustness of the DNN to noise. This article proposes a self-supervised task to increase the antiperturbation ability. This task is represented as a game between the discriminator and encoder in the adversarial module, which pulls the extracted representations into the shared distribution between the positive pairs to discard the perturbation representations and learn the invariant representations. The spatiotemporal contrastive module combines spatiotemporal prediction and patient discrimination to learn the spatiotemporal and semantic representations of categories. To learn category representations effectively, this article only uses patient-level positive pairs and alternately uses the predictor and the stop-gradient to avoid model collapse. To verify the effectiveness of the proposed method, various groups of experiments are conducted on four ECG benchmark datasets and one clinical dataset compared with the state-of-the-art methods. Experimental results showed that the proposed method outperforms the state-of-the-art methods.

Reduced physiology-induced temporal instability achieved with variable-flip-angle fast low-angle excitation echo-planar technique with multishot echo planar time-resolved imaging.

Echo planar time-resolved imaging (EPTI) is a new imaging approach that addresses the limitations of EPI by providing high-resolution, distortion- and T2/ blurring-free imaging for functional MRI (fMRI). However, as in all multishot sequences, intershot phase variations induced by physiological processes can introduce temporal instabilities to the reconstructed time-series data. This study aims to reduce these instabilities in multishot EPTI. In conventional multishot EPTI, the time intervals between the shots comprising each slice can introduce intershot phase variations. Here, the fast low-angle excitation echo-planar technique (FLEET), in which all shots of each slice are acquired consecutively with minimal time delays, was combined with a variable flip angle (VFA) technique to improve intershot consistency and maximize signal. A recursive Shinnar-Le Roux RF pulse design algorithm was used to generate pulses for different shots to produce consistent slice profiles and signal intensities across shots. Blipped controlled aliasing in parallel imaging simultaneous multislice was also combined with the proposed VFA-FLEET EPTI to improve temporal resolution and increase spatial coverage. The temporal stability of VFA-FLEET EPTI was compared with conventional EPTI at 7 T. The results demonstrated that VFA-FLEET can provide spatial-specific increase of temporal stability. We performed high-resolution task-fMRI experiments at 7 T using VFA-FLEET EPTI, and reliable BOLD responses to a visual stimulus were detected. The intershot phase variations induced by physiological processes in multishot EPTI can manifest as specific spatial patterns of physiological noise enhancement and lead to reduced temporal stability. The VFA-FLEET technique can substantially reduce these physiology-induced instabilities in multishot EPTI acquisitions. The proposed method provides sufficient stability and sensitivity for high-resolution fMRI studies.

Shot noise and tunneling magnetoresistance in silicene-based ferromagnet/antiferromagnet/ferromagnet/p-wave superconductor junctions

We investigate the transport properties in silicene-based ferromagnet/antiferromagnet/ferromagnet/p-wave superconductor junctions within the Blonder–Tinkham–Klapwijk formalism by solving the Dirac–Bogoliubov-de Gennes equation. The results show that the conductance and the shot noise strongly depend on the p-wave superconducting pair potentials and the magnetic configurations of the ferromagnetic regions. The simultaneous diminution of the conductance and enhancement of the shot noise can be achieved with the increase of the antiferromagnetic exchange field strength. The conductance and the shot noise can be effectively modulated by the external electric field and the ferromagnetic exchange field strength, and the corresponding switch effects are also achieved. The tunneling magnetoresistance (TMR) outside the subgap energy interval is always larger than the one obtained in the subgap energy interval. When the antiferromagnetic exchange field strength is large enough, a larger TMR can be obtained by tuning the external electric field.

Harris Hawk optimization driven adaptive image encryption integrating Hilbert vibrational decomposition and chaos

Image encryption is a technique used to protect the confidentiality of digital images. Existing encryption algorithms use chaotic maps for generating random sequences. These sequences purely depend on the fixed initial parameters, which are not adaptive to different input images, thereby limiting their ability to produce the best-possible encrypted image. This paper introduces a new Harris Hawk optimization (HHO) based adaptive image encryption algorithm that integrates Hilbert vibrational decomposition (HVD), and multiple chaotic maps. The new fitness function is specifically designed to optimize the security and robustness of the encrypted image, considering statistical and differential attack parameters (NPCR, UACI, Entropy, and correlation coefficient values). HHO is used to optimize the initial parameters of chaotic maps (Henon map, Duffing map, Lorenz equation) subject to variations in the input image. In the confusion stage, the input image is decomposed into four components of same dimension but decreasing energy using HVD. Subsequently, the block based pixel scrambling is performed on alternate components using the Henon map and Duffing map. In the diffusion stage, the image from the confusion stage undergoes a first level diffusion by bitwise XOR operation with a random image generated from the Lorenz equation. Further, second level of diffusion is achieved with a complex operation generated image from the input image, enhancing its adaptability. Finally, Arnold Cat map having number of iterations determined by the input image is used for final pixel permutation to produces the encrypted image. The optimized initial parameters and input image dependent confusion and diffusion operations enable the encryption system to adapt to changes in the input image, thereby enhancing its resistance against known plain text and known cipher text attacks. Moreover, the integration of HVD with multiple chaotic maps makes the scheme more complex to decrypt with plain text attacks. The performance of proposed algorithm is evaluated on five standard test images in terms of security and quality measures. Experimental results exhibit statistical and differential attack parameters close to ideal values (with Max. Entropy≈7.9996, Min. CC≈ 10−5, Max. NPCR≈99.64, Max. UACI≈33.62). The use of multiple chaotic maps at different stages makes the key space larger. Robustness analysis demonstrates the algorithm's capability in withstanding noise and image enhancement attacks. The comparison with state of the arts metaheuristic based and classical encryption algorithms highlights the superior performance of proposed HHO driven algorithm. The significant improvement in security parameters and overall fitness compared to fixed initial parameters is achieved by optimizing the encryption process.

A high constancy and noise suppression voltage shift generator in SEIR-based BIST circuit for ADC linearity test

—The built-in self-test (BIST) circuit is designed to be highly integrated with ADC under test and tests the static linearity based on the stimulus error identification and removal (SEIR) method. A novel level shift generator is proposed for high-precision ADC testing to break the resolution limitation caused by thermal noise and non-linearity. The double sampling operation realized by the chopper circuits cancels the sampling kT/C noise, and the circuit further reduces the amplifier's thermal noise by reducing the noise bandwidth. Besides, this paper presents a new method to achieve a highly linearity-constant voltage shift by applying the negative-C technique. Implemented in 180 nm CMOS process, the simulation results show that the noise power of the voltage shift is reduced by 10 dB, and the constancy of voltage shift is only 3.7 ppm over the entire ADC input range. Benefitting from the noise and linearity performance enhancement, this BIST circuit can test 18-bit ADC to 18-bit accuracy level with one-tenth sampling points.

Novel fuzzy type-II driven modified Anisotropic Diffusion filter framework for restoration and enhancement of Rician noise corrupted MR images

Novel fuzzy type-II driven modified Anisotropic Diffusion filter framework for restoration and enhancement of Rician noise corrupted MR images

Noninterferometric rotational test of the continuous spontaneous localization model: Enhancement of the collapse noise through shape optimization

Noninterferometric rotational test of the continuous spontaneous localization model: Enhancement of the collapse noise through shape optimization

Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography

BackgroundLow-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality.ObjectiveTo evaluate the image quality of low-iodine-dose CT combined with an artificial intelligence (AI)-based contrast-boosting technique in abdominal CT, compared to a standard-iodine-dose protocol in children.Materials and methodsThis single-center retrospective study included 35 pediatric patients (mean age 9.2 years, range 1–17 years) who underwent sequential abdominal CT scans—one with a standard-iodine-dose protocol (standard-dose group, Iobitridol 350 mgI/mL) and another with a low-iodine-dose protocol (low-dose group, Iohexol 240 mgI/mL)—within a 4-month interval from January 2022 to July 2022. The low-iodine CT protocol was reconstructed using an AI-based contrast-boosting technique (contrast-boosted group). Quantitative and qualitative parameters were measured in the three groups. For qualitative parameters, interobserver agreement was assessed using the intraclass correlation coefficient, and mean values were employed for subsequent analyses. For quantitative analysis of the three groups, repeated measures one-way analysis of variance with post hoc pairwise analysis was used. For qualitative analysis, the Friedman test followed by post hoc pairwise analysis was used. Paired t-tests were employed to compare radiation dose and iodine uptake between the standard- and low-dose groups.ResultsThe standard-dose group exhibited higher attenuation, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of organs and vessels compared to the low-dose group (all P-values < 0.05 except for liver SNR, P = 0.12). However, noise levels did not differ between the standard- and low-dose groups (P = 0.86). The contrast-boosted group had increased attenuation, CNR, and SNR of organs and vessels, and reduced noise compared with the low-dose group (all P < 0.05). The contrast-boosted group showed no differences in attenuation, CNR, and SNR of organs and vessels (all P > 0.05), and lower noise (P = 0.002), than the standard-dose group. In qualitative analysis, the contrast-boosted group did not differ regarding vessel enhancement and lesion conspicuity (P > 0.05) but had lower noise (P < 0.05) and higher organ enhancement and artifacts (all P < 0.05) than the standard-dose group. While iodine uptake was significantly reduced in low-iodine-dose CT (P < 0.001), there was no difference in radiation dose between standard- and low-iodine-dose CT (all P > 0.05).ConclusionLow-iodine-dose abdominal CT, combined with an AI-based contrast-boosting technique exhibited comparable organ and vessel enhancement, as well as lesion conspicuity compared to standard-iodine-dose CT in children. Moreover, image noise decreased in the contrast-boosted group, albeit with an increase in artifacts.Graphical

A novel geometric-based bistatic range grouping algorithm for multi-target localization in distributed MIMO radar systems

In this paper, a novel bistatic range grouping algorithm is proposed in order to localize multiple targets in distributed multiple-input multiple-output (MIMO) radar systems. In a distributed MIMO radar, target localization is performed based on bistatic ranges, the distances between antennas through a target, without directional information, and grouping bistatic ranges with respect to the target is a crucial step to estimate multiple targets efficiently. In contrast to the existing grouping schemes that use tentative target estimation or complicated joint bistatic range grouping and target estimation, the proposed algorithm uses only the geometric property of bistatic ranges, resulting in computationally efficient and robust grouping algorithm without noise enhancement, especially for realistic noisy environments where other existing grouping algorithms often fail. The performance of the proposed scheme is confirmed by evaluating the probability of miss detection and the probability of false alarm through numerical simulations for various environments including indirect paths and blocked paths. The root mean square error performance of the multi-target localization using the proposed bistatic grouping algorithm is also presented for realistic noisy environments.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Correlated noise enhancement of coherence and fidelity in coupled qubits

ABSTRACT It is generally assumed that environmental noise arising from thermal fluctuations are detrimental to preserving coherence and entanglement in a quantum system. In the simplest sense, dephasing and decoherence are tied to energy fluctuations driven by coupling between the system and the normal modes of the bath. Here, we explore the role of noise correlation in an open-loop model quantum communication system whereby the ‘sender’ and the ‘receiver’ are subject to local environments with various degrees of correlation or anti-correlation. We introduce correlation within the spectral density by solving multidimensional stochastic differential equations and introduce these into the Redfield equations of motion for the system density matrix. We find that correlation can enhance both the fidelity and purity of a maximally entangled (Bell) state. Moreover, by comparing the evolution of different initial Bell states, we show that one can effectively probe the correlation between two local environments. These observations may be useful in the design of high-fidelity quantum gates and communication protocols.

Dual-student knowledge distillation for visual anomaly detection

Anomaly detection poses a significant challenge in the industry and knowledge distillation constructed using a frozen teacher network and a trainable student network is the prevailing approach for detecting suspicious regions. Forward and reverse distillation are the main ways to achieve anomaly detection. To design an effective model and aggregate detection results, we propose a dual-student knowledge distillation (DSKD) based on forward and reverse distillation. Taking advantage of the priority of reverse distillation to obtain high-level representation, we combine a skip connection and an attention module to build a reverse distillation student network that simultaneously focuses on high-level representation and low-level features. DSKD uses a forward distillation network as an auxiliary to allow the student network to preferentially obtain the query image. For different anomaly score maps obtained by the dual-student network, we use synthetic noise enhancement in combination with image segmentation loss to adaptively learn the weight scores of individual maps. Empirical experiments conducted on the MVTec dataset show that the proposed DSKD method achieves good performance on texture images as well as competitive results on object images compared with other state-of-the-art methods. Meanwhile, ablation experiments and a visualization analysis validate the contributions of each of the model’s components.

Rotational Doppler effect of vortex beam with frequency-shifted laser feedback

We propose and demonstrate a rotational Doppler measurement system based on frequency-shifted laser feedback interferometry. The system utilizes a vortex beam as the probe, which is modulated by the target and then reflected back into the laser cavity. Compared with the conventional rotational Doppler methods, this approach effectively enhances the amplitude of the generated beat signal through intra-cavity interference between the echo signal and local oscillator. Consequently, it improves sensitivity in weak-signal detection and enables measurement with low probe-beam power. A signal to noise (SNR) enhancement of over 15 dB was observed compare to the Mach-Zehnder structure. This system inherits the advantages of rotational Doppler effect, possessing the capacity to get a frequency proportional to the topological charge of vortex. Experimental validation demonstrates accurate measurement of rotational angle and velocity, with the ability to distinguish rotational direction through heterodyne detection. For vortices with topological charge ranging from 18 to 21 and rotational speeds in range of 150 to 600 revolutions per minute (RPM), the measurement error is within ±3 RPM. Overall, the proposed system possesses high sensitivity to echo signal, low photon consumption, and direction information availability, which provides a new approach for optimizing the rotational Doppler effect in practical applications.

Lung Cancer Detection in CT Images Using Deep Learning Techniques: A Survey Review

INTRODUCTION: The Computed Tomography (CT) imaging-based Lung cancer detection is crucial for early diagnosis. This survey paper presents an overview of the techniques and advancements in CT-based lung cancer detection. It covers the fundamentals of CT imaging, including principles, types, and protocols. &#x0D; OBJECTIVES: The paper explores image processing techniques for pre-processing, such as noise reduction, enhancement, and segmentation. &#x0D; METHODS: Additionally, it discusses feature extraction methods, including shape, texture, and intensity-based features, as well as Deep Learning (DL) and Machine Learning (ML) methods for automated classification. &#x0D; RESULTS: Computerised systems and their integration is examined with CT imaging along with performance evaluation metrics. The survey concludes by addressing challenges, limitations, and future directions. The imaging modalities and artificial intelligence techniques are used to improve lung cancer detection. &#x0D; CONCLUSION: This comprehensive survey aims to provide a concise understanding of CT-based lung cancer detection for researchers and healthcare professionals.

Sonar Image Target Detection Based on Simulated Stain-like Noise and Shadow Enhancement in Optical Images under Zero-Shot Learning

There are many challenges in using side-scan sonar (SSS) images to detect objects. The challenge of object detection and recognition in sonar data is greater than in optical images due to the sparsity of detectable targets. The complexity of real-world underwater scanning presents additional difficulties, as different angles produce sonar images of varying characteristics. This heterogeneity makes it difficult for algorithms to accurately identify and detect sonar objects. To solve these problems, this paper presents a novel method for sonar image target detection based on a transformer and YOLOv7. Thus, two data augmentation techniques are introduced to improve the performance of the detection system. The first technique applies stain-like noise to the training optical image data to simulate the real sonar image environment. The second technique adds multiple shadows to the optical image and 3D data targets to represent the direction of the target in the sonar image. The proposed method is evaluated on a public sonar image dataset, and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed. The experimental results show that our method achieves better precision.

An implementation and evaluation of sound activated noise display for the reduction of noise levels and enhancement of staff’s sleep and noise awareness

An implementation and evaluation of sound activated noise display for the reduction of noise levels and enhancement of staff’s sleep and noise awareness

Eliminating intrinsic interference in GFDM systems: Design of a complex-valued prototype filter

Prototype filter design is a critical challenge in generalized frequency division multiplexing (GFDM) systems. Poorly designed filters can result in inherent and out-of-band (OOB) interferences, severely impacting system performance. This paper proposes a novel solution to this problem by introducing an optimal prototype filter that mitigates the adverse effects of intrinsic interferences in GFDM systems. Our approach involves using a complex-valued prototype filter, which resembles a single-sideband (SSB) modulation scheme and significantly enhances bandwidth efficiency compared to conventional GFDM. To design the optimal filter, we formulate an optimization problem that eliminates inband and adjacent subcarriers intrinsic interferences. We also provide analytical expressions for evaluating the system’s bit error rate (BER) and demonstrate the superiority of our optimized prototype filter over its current counterparts. To validate the design of the proposed prototype filter, we assess the noise enhancement factor (NEF). Our results indicate that our proposed filter improves system performance and reduces BER in GFDM systems.

AI for PET image reconstruction.

Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the data statistics and improved modelling of the imaging physics. However, high noise and limited spatial resolution have remained issues in PET imaging, and state-of-the-art PET reconstruction has started to exploit other medical imaging modalities (such as MRI) to assist in noise reduction and enhancement of PET's spatial resolution. Nonetheless, there is an ongoing drive towards not only improving image quality, but also reducing the injected radiation dose and reducing scanning times. While the arrival of new PET scanners (such as total body PET) is helping, there is always a need to improve reconstructed image quality due to the time and count limited imaging conditions. Artificial intelligence (AI) methods are now at the frontier of research for PET image reconstruction. While AI can learn the imaging physics as well as the noise in the data (when given sufficient examples), one of the most common uses of AI arises from exploiting databases of high-quality reference examples, to provide advanced noise compensation and resolution recovery. There are three main AI reconstruction approaches: (i) direct data-driven AI methods which rely on supervised learning from reference data, (ii) iterative (unrolled) methods which combine our physics and statistical models with AI learning from data, and (iii) methods which exploit AI with our known models, but crucially can offer benefits even in the absence of any example training data whatsoever. This article reviews these methods, considering opportunities and challenges of AI for PET reconstruction.